Diversity in the genetic lesions that drive cancer initiation and progression is extreme. This nefarious complexity is, in large measure, responsible for the capacity of neoplastic disease to evade current best efforts for effective therapy. Personalized medicine has been proposed in response to this conundrum as a mechanism to tailor cancer treatment to a specific tumor's genetic and epigenetic characteristics. However, selection of appropriate treatment is dramatically limited by the paucity of available drugs and by the difficulty of linking treatment options to the appropriate patients. The challenge is to identiy authentic intervention targets for development of an appropriately diverse cohort of therapies to contend with disease heterogeneity together with molecular enrollment biomarkers that specify patient populations responsive to those therapies. We are addressing this challenge by a focused investigation of conditional vulnerabilities that arise as a consequence of oncogene expression and tumor evolution. To accomplish this, we have constructed a cancer intervention discovery pipeline using parallel genetic and chemical perturbations within a large, fully molecularly annotated, panel of cell lines representative of the somatic lesions detected in lung cancer by national and international cancer genome sequencing efforts. We have found that current first line targeted therapies are discoverable within this panel together with the enrollment biomarkers required to effectively stratify responsive patients. Importantly, we have found that new genetic and chemical vulnerabilities can be revealed that are linked to recurrent mutations in lung cancer patients that are not currently actionable. We are leveraging this approach to 1) parse mechanistic lung cancer subtypes and 2) elaborate new intervention targets and chemical probes that are linked to those subtypes by robust molecular discriminators.